From e89a2ae027d4522b6f7c478cc534f2f983fd6670 Mon Sep 17 00:00:00 2001
From: PythonFZ <fabian.zills@web.de>
Date: Thu, 5 Feb 2026 14:38:42 +0100
Subject: [PATCH 1/4] Refactor YehHummer to use linear (D_0, slope)
 parameterization

---
 kinisi/fitting.py    |   4 ++
 kinisi/yeh_hummer.py | 118 ++++++++++++++++++-------------------------
 2 files changed, 52 insertions(+), 70 deletions(-)

diff --git a/kinisi/fitting.py b/kinisi/fitting.py
index 5ff1573..dd6b3bb 100644
--- a/kinisi/fitting.py
+++ b/kinisi/fitting.py
@@ -112,6 +112,10 @@ def log_likelihood(self, parameters: tuple[float]) -> float:
         x_values = self.get_independent_variable()
         model = self.function(x_values, *parameters)
 
+        # Handle invalid model values (e.g., from VTF when T0 >= T)
+        if np.any(~np.isfinite(model)) or np.any(model <= 0):
+            return -np.inf
+
         covariance_matrix = np.diag(self.data.variances)
         y_values = self.data.values
 
diff --git a/kinisi/yeh_hummer.py b/kinisi/yeh_hummer.py
index 961caf0..3f9eeb6 100644
--- a/kinisi/yeh_hummer.py
+++ b/kinisi/yeh_hummer.py
@@ -16,6 +16,7 @@
 
 from kinisi import __version__
 from kinisi.fitting import FittingBase
+from kinisi.samples import Samples
 
 from .due import Doi, due
 
@@ -34,9 +35,12 @@ class YehHummer(FittingBase):
     The Yeh-Hummer correction formula is:
     D_PBC = D_0 - (k_B * T * xi) / (6 * pi * eta * L)
 
+    Internally, fitting is performed in (D_0, slope) space where the model is linear,
+    then slope is converted to viscosity for output.
+
     :param diffusion: sc.DataArray with diffusion coefficients and box_length coordinate
     :param temperature: Temperature (will be extracted from coords if not provided)
-    :param bounds: Optional bounds for [D_0, viscosity] parameters
+    :param bounds: Optional bounds for [D_0, viscosity] parameters (viscosity in Pa*s)
     """
 
     def __init__(self, diffusion, temperature: sc.Variable, bounds=None):
@@ -48,69 +52,54 @@ def __init__(self, diffusion, temperature: sc.Variable, bounds=None):
 
         # Constants
         self.xi_cubic = 2.837297  # Ewald constant for cubic boxes
-        # Set up parameters for YehHummer fitting
-        parameter_names = ('D_0', 'viscosity')
-        parameter_units = (diffusion.unit, sc.Unit('Pa*s'))
 
-        # Set default bounds if not provided
+        # Slope has units of [diffusion] * [length] = cm^2/s * Å
+        self._slope_unit = diffusion.unit * self.box_lengths.unit
+
+        # Set up parameters for fitting - use (D_0, slope) for linear model
+        parameter_names = ('D_0', 'slope')
+        parameter_units = (diffusion.unit, self._slope_unit)
+
+        # Compute bounds: use provided or defaults
         if bounds is None:
-            # Auto-generate reasonable bounds
             D_max = np.max(diffusion.values)
-            bounds = (
-                (D_max * 0.8 * diffusion.unit, D_max * 2.0 * diffusion.unit),
-                (1e-5 * sc.Unit('Pa*s'), 1e-1 * sc.Unit('Pa*s')),
-            )
+            D_bounds = (D_max * 0.8 * diffusion.unit, D_max * 2.0 * diffusion.unit)
+            visc_lower, visc_upper = 1e-5 * sc.Unit('Pa*s'), 1e-1 * sc.Unit('Pa*s')
+        else:
+            D_bounds = (bounds[0][0].to(unit=parameter_units[0]), bounds[0][1].to(unit=parameter_units[0]))
+            visc_lower, visc_upper = bounds[1]
+
+        # Higher viscosity = lower slope, so bounds are inverted
+        slope_bounds = (
+            self.viscosity_to_slope(visc_upper) * self._slope_unit,
+            self.viscosity_to_slope(visc_lower) * self._slope_unit,
+        )
 
-        # Initialize base class with custom function
+        # Initialize base class with linear function
         super().__init__(
             data=diffusion,
             function=self._yeh_hummer_function,
             parameter_names=parameter_names,
             parameter_units=parameter_units,
-            bounds=bounds,
+            bounds=[D_bounds, slope_bounds],
             coordinate_name='box_length',
         )
 
-        # Convert bounds to values for optimization (for backward compatibility)
-        self.bounds_values = tuple(
-            [
-                (b[0].to(unit=u).value, b[1].to(unit=u).value)
-                for b, u in zip(self.bounds, self.parameter_units, strict=False)
-            ]
-        )
-
     def _yeh_hummer_function(
         self,
         box_lengths: np.ndarray,
         D_0: float,
-        viscosity: float,
+        slope: float,
     ) -> np.ndarray:
         """
-        Yeh-Hummer function for finitie-size correction fit.
+        Yeh-Hummer function for finite-size correction fit (linear form).
+        D_PBC = D_0 - slope / L
 
-        :param box_lengths: Array of box lengths / Å
+        :param box_lengths: Array of box lengths / Angstrom
         :param D_0: Infinite-system diffusion coefficient
-        :param viscosity: Shear viscosity
+        :param slope: Slope = (k_B * T * xi) / (6 * pi * eta)
         """
-        # Handle both scalar and array inputs
-        box_lengths = np.asarray(box_lengths)
-        viscosity = np.asarray(viscosity)
-
-        inv_L = 1.0 / box_lengths
-
-        if viscosity.ndim == 0:
-            eta_with_unit = viscosity * self.parameter_units[1]
-            slope = self.viscosity_to_slope(eta_with_unit)
-        else:
-            # viscosity as an array (from MCMC samples)
-            slopes = []
-            for visc_val in viscosity:
-                eta_with_unit = visc_val * self.parameter_units[1]
-                slope = self.viscosity_to_slope(eta_with_unit)
-                slopes.append(slope)
-            slope = np.array(slopes)
-
-        return self.yeh_hummer_linear(inv_L, D_0, slope)
+        return D_0 - slope / np.asarray(box_lengths)
 
     def _prepare_data_for_fit(self):
         """Prepare data in correct format for fitting."""
@@ -131,10 +120,8 @@ def _slope_to_viscosity(self, slope):
 
         k_B_T = sc.to_unit(const.Boltzmann * self.temperature, 'J')
 
-        # slope has units of [diffusion] / [1/length] = [diffusion] * [length]
-        # diffusion is cm^2/s, box_lengths is Å, so slope * diffusion.unit / (1/box_lengths.unit)
-        # This gives us (cm^2/s) / (1/Å) = cm^2/s * Å = cm^2 * Å / s
-        slope_with_units = slope * self.diffusion.unit / (1 / self.box_lengths.unit)
+        # slope has units of [diffusion] * [length]
+        slope_with_units = slope * self._slope_unit
         slope_SI = sc.to_unit(slope_with_units, 'm^3/s')
 
         eta = (k_B_T * self.xi_cubic) / (6 * np.pi * slope_SI)
@@ -167,11 +154,8 @@ def linear_func(x, a, b):
         D_0_init = popt[0]
         slope_init = popt[1]
 
-        # Convert slope to viscosity
-        eta_init = self._slope_to_viscosity(slope_init).value
-
         # Use these as initial parameters for optimization
-        x0 = [D_0_init, eta_init]
+        x0 = [D_0_init, slope_init]
 
         from scipy.optimize import minimize
 
@@ -181,7 +165,7 @@ def linear_func(x, a, b):
 
         # Store results
         self.data_group['D_0'] = result.x[0] * self.parameter_units[0]
-        self.data_group['viscosity'] = result.x[1] * self.parameter_units[1]
+        self.data_group['slope'] = result.x[1] * self.parameter_units[1]
 
     @property
     def D_infinite(self):
@@ -189,22 +173,16 @@ def D_infinite(self):
         return self.data_group['D_0']
 
     @property
-    def shear_viscosity(self):
-        """Return estimated shear viscosity."""
-        return self.data_group['viscosity']
-
-    @staticmethod
-    def yeh_hummer_linear(inv_L, D_0, slope):
-        """
-        Linear form of Yeh-Hummer equation for fitting.
-
-        D_PBC = D_0 - slope * (1/L)
-
-        where slope = (k_B * T * xi) / (6 * pi * eta)
+    def shear_viscosity(self) -> sc.Variable | Samples:
+        """Return estimated shear viscosity (converted from slope samples)."""
+        slope_data = self.data_group['slope']
+        if isinstance(slope_data, Samples):
+            # Convert each slope sample to viscosity
+            viscosities = []
+            for s in slope_data.values:
+                eta = self._slope_to_viscosity(s)
+                viscosities.append(eta.value)
+            return Samples(np.array(viscosities), unit=sc.Unit('Pa*s'))
+        else:
+            return self._slope_to_viscosity(slope_data.value)
 
-        :param inv_L: Inverse box lengths (1/L)
-        :param D_0: Infinite-system diffusion coefficient
-        :param slope: Slope containing viscosity information
-        :return: D_PBC values
-        """
-        return D_0 - slope * inv_L

From 53ff8cf872f70ac365f1759a4a4360ef8ec31ab0 Mon Sep 17 00:00:00 2001
From: PythonFZ <fabian.zills@web.de>
Date: Thu, 5 Feb 2026 14:54:56 +0100
Subject: [PATCH 2/4] lint

---
 kinisi/yeh_hummer.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/kinisi/yeh_hummer.py b/kinisi/yeh_hummer.py
index 3f9eeb6..77cec98 100644
--- a/kinisi/yeh_hummer.py
+++ b/kinisi/yeh_hummer.py
@@ -185,4 +185,3 @@ def shear_viscosity(self) -> sc.Variable | Samples:
             return Samples(np.array(viscosities), unit=sc.Unit('Pa*s'))
         else:
             return self._slope_to_viscosity(slope_data.value)
-

From e5fb13b6b84735946ae86d173c227576d29e0567 Mon Sep 17 00:00:00 2001
From: PythonFZ <fabian.zills@web.de>
Date: Thu, 5 Feb 2026 15:40:12 +0100
Subject: [PATCH 3/4] tests

---
 kinisi/tests/test_yeh_hummer.py | 26 +++++++++++++++++++-------
 kinisi/yeh_hummer.py            |  2 ++
 2 files changed, 21 insertions(+), 7 deletions(-)

diff --git a/kinisi/tests/test_yeh_hummer.py b/kinisi/tests/test_yeh_hummer.py
index 5ee1435..92d48cf 100644
--- a/kinisi/tests/test_yeh_hummer.py
+++ b/kinisi/tests/test_yeh_hummer.py
@@ -12,14 +12,24 @@
 class TestYehHummer:
     """Tests for the YehHummer class."""
 
-    def test_yeh_hummer_linear(self):
-        """Test the linear Yeh-Hummer function."""
-        inv_L = np.array([0.04, 0.05, 0.06])
+    def test_yeh_hummer_function(self):
+        """Test the Yeh-Hummer fitting function."""
+        box_lengths = np.array([20.0, 30.0, 40.0])
+        D_values = np.array([5.0e-5, 5.2e-5, 5.4e-5])
+        D_errors = np.array([0.1e-5, 0.1e-5, 0.1e-5])
+
+        td = sc.DataArray(
+            data=sc.array(dims=['system'], values=D_values, variances=D_errors**2, unit='cm^2/s'),
+            coords={'box_length': sc.Variable(dims=['system'], values=box_lengths, unit='angstrom')},
+        )
+
+        yh = YehHummer(td, temperature=sc.scalar(298, unit='K'))
+
+        # Test the internal function: D_PBC = D_0 - slope / L
         D_0 = 6.0e-5
         slope = 1.0e-6
-
-        result = YehHummer.yeh_hummer_linear(inv_L, D_0, slope)
-        expected = D_0 - slope * inv_L
+        result = yh._yeh_hummer_function(box_lengths, D_0, slope)
+        expected = D_0 - slope / box_lengths
 
         np.testing.assert_array_almost_equal(result, expected)
 
@@ -119,7 +129,9 @@ def test_yeh_hummer_properties(self):
 
         # Test property accessors
         assert yh.D_infinite == yh.data_group['D_0']
-        assert yh.shear_viscosity == yh.data_group['viscosity']
+        # slope is stored in data_group, viscosity is computed from slope
+        assert 'slope' in yh.data_group.keys()
+        assert yh.shear_viscosity.unit == sc.Unit('Pa*s')
 
         # Test that the object has string representations
         assert len(str(yh)) > 0
diff --git a/kinisi/yeh_hummer.py b/kinisi/yeh_hummer.py
index 77cec98..f3cfe49 100644
--- a/kinisi/yeh_hummer.py
+++ b/kinisi/yeh_hummer.py
@@ -66,6 +66,8 @@ def __init__(self, diffusion, temperature: sc.Variable, bounds=None):
             D_bounds = (D_max * 0.8 * diffusion.unit, D_max * 2.0 * diffusion.unit)
             visc_lower, visc_upper = 1e-5 * sc.Unit('Pa*s'), 1e-1 * sc.Unit('Pa*s')
         else:
+            if len(bounds) != 2:
+                raise ValueError('Bounds must be a tuple of length 2: (D_0_bounds, viscosity_bounds)')
             D_bounds = (bounds[0][0].to(unit=parameter_units[0]), bounds[0][1].to(unit=parameter_units[0]))
             visc_lower, visc_upper = bounds[1]
 

From 37c9aa17674fed1a637325db10fe53472c4cb4fc Mon Sep 17 00:00:00 2001
From: Fabian Zills <fzills@icp.uni-stuttgart.de>
Date: Wed, 11 Feb 2026 16:48:22 +0100
Subject: [PATCH 4/4] fix docs; address comments

---
 docs/source/yeh_hummer.ipynb | 533 ++---------------------------------
 kinisi/fitting.py            |   4 -
 kinisi/yeh_hummer.py         |  20 +-
 3 files changed, 27 insertions(+), 530 deletions(-)

diff --git a/docs/source/yeh_hummer.ipynb b/docs/source/yeh_hummer.ipynb
index 409af6d..6aef68e 100644
--- a/docs/source/yeh_hummer.ipynb
+++ b/docs/source/yeh_hummer.ipynb
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "imports",
    "metadata": {},
    "outputs": [],
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "tip4p-data",
    "metadata": {},
    "outputs": [],
@@ -70,47 +70,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "create-dataarray",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><!-- Original source from -->\n",
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-       "<svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
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!important;stroke-width:0;stroke:currentColor;fill:currentColor;}label.sc-hide-icon svg{opacity:0;}.sc-standalone-var-name{grid-column:1/3;}.sc-standalone-var-name span{padding-left:25px;padding-right:10px;}.sc-title{font-weight:bold;font-size:1.5em;}.sc-subtitle{font-weight:normal;font-style:italic;text-align:left;font-size:1.2em;padding:1px;}.sc-label{fill:var(--sc-font-color0,#444444);text-anchor:middle;}.sc-name{fill:var(--sc-font-color0,#111111);}.sc-inset-line{stroke:var(--sc-font-color1);stroke-width:0.05;stroke-dasharray:0.2,0.2;}.sc-log-wrap{height:25ex;resize:vertical;overflow-y:scroll;display:flex;flex-direction:column-reverse;border:1px solid;border-color:var(--jp-border-color2);background-color:var(--sc-background-color1);}div.sc-log{line-height:2.5ex;}table.sc-log{table-layout:auto;border-collapse:collapse;}tr.sc-log:nth-child(even){background-color:var(--sc-background-color0);}tr.sc-log > td{vertical-align:top;padding-bottom:0.5ex;}.sc-log-time-stamp{min-width:22ch;font-family:var(--jp-code-font-family);color:var(--sc-font-color2);}.sc-log-level{min-width:10ch;}tr.sc-log-debug td.sc-log-level{color:var(--jp-accent-color1);}tr.sc-log-info td.sc-log-level{color:var(--jp-info-color1);}tr.sc-log-warning td.sc-log-level{color:var(--jp-warn-color1);}tr.sc-log-error td.sc-log-level{font-weight:bold;color:var(--jp-error-color2);}tr.sc-log-critical td.sc-log-level{font-weight:bold;color:var(--sc-background-color0);background-color:var(--jp-error-color1);}.sc-log-message{white-space:pre-wrap;width:100%;}.sc-log-html-payload{white-space:normal;}.sc-log-name{padding-right:0.5em;text-align:right;white-space:pre-wrap;color:var(--sc-font-color3);}</style><div class='sc-wrap sc-root'><div class='sc-header'><div class='sc-obj-type'>scipp.DataArray (1001 Bytes)</div></div><ul class='sc-sections'><li class='sc-section-item'><input id='section-88af516a-1c71-44c7-ac80-198cca8ffdf3' class='sc-section-summary-in' type='checkbox' disabled ><label for='section-88af516a-1c71-44c7-ac80-198cca8ffdf3' class='sc-section-summary' >Dimensions:</label><div class='sc-section-inline-details'><ul class='sc-dim-list'><li><span>system</span>: 5</li></ul></div><div class='sc-section-details'></div></li><li class='sc-section-item'><input id='section-930d083f-3206-40d9-b834-ce6cf0c690d3' class='sc-section-summary-in' type='checkbox'  checked><label for='section-930d083f-3206-40d9-b834-ce6cf0c690d3' class='sc-section-summary'  title='Expand/collapse section'>Coordinates: <span>(1)</span></label><div class='sc-section-inline-details'></div><div class='sc-section-details'><ul class='sc-var-list'><li class='sc-var-item'><div class='sc-var-name'><span class='sc-aligned'>box_length</span></div><div class='sc-var-dims'>(system)</div><div class='sc-var-dtype'>float64</div><div class='sc-var-unit'>Å</div><div class='sc-value-preview sc-preview'><span><div>18.58, 23.42, 29.51, 37.19, 46.86</div></span></div><input id='attrs-1fa47010-1b50-4212-ab95-0e236ba5ce42' class='sc-var-attrs-in' type='checkbox' disabled><label for='attrs-1fa47010-1b50-4212-ab95-0e236ba5ce42' class='sc-hide-icon' title='Show/Hide attributes'><svg class='icon sc-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e053a004-0d86-4709-bc43-11244c9be966' class='sc-var-data-in' type='checkbox'><label for='data-e053a004-0d86-4709-bc43-11244c9be966' title='Show/Hide data repr'><svg class='icon sc-icon-database'><use xlink:href='#icon-database'></use></svg></label><pre class='sc-var-data'>Values:<br>array([18.58, 23.42, 29.51, 37.19, 46.86])</pre></span></li></ul></div></li><li class='sc-section-item'><input id='section-534a2b3d-30ea-4387-bb66-6f7ac0414bfa' class='sc-section-summary-in' type='checkbox'  checked><label for='section-534a2b3d-30ea-4387-bb66-6f7ac0414bfa' class='sc-section-summary'  title='Expand/collapse section'>Data: <span>(1)</span></label><div class='sc-section-inline-details'></div><div class='sc-section-details'><ul class='sc-var-list'><li class='sc-var-item'><div class='sc-var-name'><span></span></div><div class='sc-var-dims'>(system)</div><div class='sc-var-dtype'>float64</div><div class='sc-var-unit'>cm^2/s</div><div class='sc-value-preview sc-preview'><span><div>4.884e-05, 5.123e-05, 5.315e-05, 5.466e-05, 5.590e-05</div></span><span><div>σ = 3.200e-07, 2.700e-07, 1.400e-07, 1.100e-07, 1.300e-07</div></span></div><input id='attrs-d55043bd-13fa-4f38-86c0-e5a67e928451' class='sc-var-attrs-in' type='checkbox' disabled><label for='attrs-d55043bd-13fa-4f38-86c0-e5a67e928451' class='sc-hide-icon' title='Show/Hide attributes'><svg class='icon sc-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0c92be7-f898-47ae-b56d-ec526b3f9c43' class='sc-var-data-in' type='checkbox'><label for='data-b0c92be7-f898-47ae-b56d-ec526b3f9c43' title='Show/Hide data repr'><svg class='icon sc-icon-database'><use xlink:href='#icon-database'></use></svg></label><pre class='sc-var-data'>Values:<br>array([4.884e-05, 5.123e-05, 5.315e-05, 5.466e-05, 5.590e-05])<br><br>Variances (σ²):<br>array([1.024e-13, 7.290e-14, 1.960e-14, 1.210e-14, 1.690e-14])</pre></li></ul></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<scipp.DataArray>\n",
-       "Dimensions: Sizes[system:5, ]\n",
-       "Coordinates:\n",
-       "* box_length                float64             [Å]  (system)  [18.58, 23.42, ..., 37.19, 46.86]\n",
-       "Data:\n",
-       "                            float64         [cm^2/s]  (system)  [4.884e-05, 5.123e-05, ..., 5.466e-05, 5.59e-05]  [1.024e-13, 7.29e-14, ..., 1.21e-14, 1.69e-14]\n"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "td = sc.DataArray(\n",
     "    data=sc.array(dims=['system'], values=D_values, variances=D_errors**2, unit='cm^2/s'),\n",
@@ -131,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "create-yeh-hummer",
    "metadata": {},
    "outputs": [],
@@ -152,25 +115,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "run-mcmc",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "MCMC Sampling:   0%|          | 0/1000 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "MCMC Sampling: 100%|██████████| 1000/1000 [00:01<00:00, 517.21it/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "yh.mcmc(n_samples=500, n_walkers=16)"
    ]
@@ -185,21 +133,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "corner-plot",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 550x550 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from corner import corner\n",
     "\n",
@@ -222,442 +159,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "show-d-infinite",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><!-- Original source from -->\n",
-       "<!-- https://github.com/jsignell/xarray/blob/1d960933ab252e0f79f7e050e6c9261d55568057/xarray/static/html/icons-svg-inline.html -->\n",
-       "<svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-       "<defs>\n",
-       "<symbol id=\"icon-database\" viewbox=\"0 0 32 32\">\n",
-       "<title>Show/Hide data repr</title>\n",
-       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
-       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "</symbol>\n",
-       "<symbol id=\"icon-file-text2\" viewbox=\"0 0 32 32\">\n",
-       "<title>Show/Hide attributes</title>\n",
-       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
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td{vertical-align:top;padding-bottom:0.5ex;}.sc-log-time-stamp{min-width:22ch;font-family:var(--jp-code-font-family);color:var(--sc-font-color2);}.sc-log-level{min-width:10ch;}tr.sc-log-debug td.sc-log-level{color:var(--jp-accent-color1);}tr.sc-log-info td.sc-log-level{color:var(--jp-info-color1);}tr.sc-log-warning td.sc-log-level{color:var(--jp-warn-color1);}tr.sc-log-error td.sc-log-level{font-weight:bold;color:var(--jp-error-color2);}tr.sc-log-critical td.sc-log-level{font-weight:bold;color:var(--sc-background-color0);background-color:var(--jp-error-color1);}.sc-log-message{white-space:pre-wrap;width:100%;}.sc-log-html-payload{white-space:normal;}.sc-log-name{padding-right:0.5em;text-align:right;white-space:pre-wrap;color:var(--sc-font-color3);}</style><div class=\"sc-wrap sc-root\"><div class=\"sc-header\"><div class=\"sc-obj-type\">kinisi.Samples</div></div><ul class=\"sc-sections\"><li class=\"sc-section-item\"><ul class=\"sc-var-list\"><li class=\"sc-var-item\"><div class=\"sc-standalone-var-name\"><span>(samples: 800)</span></div><div class=\"sc-var-dtype\">float64</div><div class=\"sc-var-unit\">cm^2/s</div><div class=\"sc-value-preview sc-preview\">(6.052+/-0.025)e-05</div><input class=\"sc-var-attrs-in\" disabled=\"\" id=\"attrs-6565d7ba-5277-4ad1-b90d-2c747955a7a8\" type=\"checkbox\"/><label class=\"sc-hide-icon\" for=\"attrs-6565d7ba-5277-4ad1-b90d-2c747955a7a8\" title=\"Show/Hide attributes\"><svg class=\"icon sc-icon-file-text2\"><use xlink:href=\"#icon-file-text2\"></use></svg></label><input class=\"sc-var-data-in\" id=\"data-37d890f9-f493-492f-afb0-05240d98815f\" type=\"checkbox\"/><label for=\"data-37d890f9-f493-492f-afb0-05240d98815f\" title=\"Show/Hide data repr\"><svg class=\"icon sc-icon-database\"><use xlink:href=\"#icon-database\"></use></svg></label><pre class=\"sc-var-data\">Values:<br/>array([6.04125834e-05, 6.07504100e-05, 5.99169775e-05, 6.09137864e-05,\n",
-       "       5.99874383e-05, 6.03256851e-05, 5.98389338e-05, 6.07141440e-05,\n",
-       "       6.10288879e-05, 6.09424864e-05, 6.03589779e-05, 6.03720490e-05,\n",
-       "       6.06000336e-05, 6.04003056e-05, 6.07848832e-05, 6.02471869e-05,\n",
-       "       6.03400301e-05, 6.04554940e-05, 6.02584704e-05, 6.08654739e-05,\n",
-       "       6.02971862e-05, 6.04491714e-05, 6.02986903e-05, 6.12246695e-05,\n",
-       "       6.08398247e-05, 6.08549727e-05, 6.04107947e-05, 6.05373335e-05,\n",
-       "       6.03253096e-05, 6.02377685e-05, 6.03661254e-05, 6.05224893e-05,\n",
-       "       6.10262188e-05, 6.04719532e-05, 6.05640260e-05, 6.06625534e-05,\n",
-       "       6.03575727e-05, 6.09149260e-05, 6.00522500e-05, 6.07028130e-05,\n",
-       "       6.02267145e-05, 6.09071947e-05, 6.05680368e-05, 6.04911293e-05,\n",
-       "       6.06326397e-05, 6.01270913e-05, 6.02305303e-05, 6.04837284e-05,\n",
-       "       6.07068429e-05, 6.03506553e-05, 6.05854482e-05, 6.09073994e-05,\n",
-       "       6.09705809e-05, 6.05081049e-05, 6.04688691e-05, 6.05382389e-05,\n",
-       "       6.04481647e-05, 6.06392688e-05, 6.04933576e-05, 6.04403576e-05,\n",
-       "       6.03988573e-05, 6.05285357e-05, 6.03060898e-05, 6.04388407e-05,\n",
-       "       6.07459924e-05, 6.03081247e-05, 6.04751883e-05, 6.07644579e-05,\n",
-       "       6.08985516e-05, 6.07832266e-05, 6.01068134e-05, 6.04027392e-05,\n",
-       "       6.04165354e-05, 6.10703316e-05, 6.04927215e-05, 6.03477204e-05,\n",
-       "       6.00936741e-05, 6.03952275e-05, 6.03146231e-05, 6.04934654e-05,\n",
-       "       6.06003827e-05, 6.02899172e-05, 6.03925117e-05, 6.11510341e-05,\n",
-       "       6.04234152e-05, 6.05475343e-05, 6.02119432e-05, 6.06527083e-05,\n",
-       "       6.05444251e-05, 6.11183134e-05, 6.04415818e-05, 6.03161453e-05,\n",
-       "       6.00573991e-05, 6.06556159e-05, 6.03131590e-05, 6.02390668e-05,\n",
-       "       6.02061403e-05, 6.03220637e-05, 6.08006274e-05, 6.09052066e-05,\n",
-       "       6.05341572e-05, 6.01628393e-05, 6.03137711e-05, 6.04429703e-05,\n",
-       "       6.07427724e-05, 6.05564371e-05, 6.03246035e-05, 6.02795141e-05,\n",
-       "       6.08648117e-05, 6.05976757e-05, 6.03743355e-05, 6.01117599e-05,\n",
-       "       6.03759164e-05, 6.02925930e-05, 6.09820959e-05, 6.04993573e-05,\n",
-       "       6.05258439e-05, 6.05122561e-05, 6.04692470e-05, 6.04596236e-05,\n",
-       "       6.09132967e-05, 6.05722619e-05, 6.02970309e-05, 6.03757722e-05,\n",
-       "       6.10120388e-05, 6.05913093e-05, 6.04370511e-05, 6.02628008e-05,\n",
-       "       6.06665639e-05, 6.04411931e-05, 6.10409616e-05, 6.05614203e-05,\n",
-       "       6.03642815e-05, 6.06263138e-05, 6.05590246e-05, 6.04100732e-05,\n",
-       "       6.05840539e-05, 6.04627513e-05, 6.00914958e-05, 6.04238997e-05,\n",
-       "       6.08153217e-05, 6.05730661e-05, 6.02939062e-05, 6.01377339e-05,\n",
-       "       6.10157535e-05, 6.02365375e-05, 6.08247891e-05, 6.05523774e-05,\n",
-       "       6.00753965e-05, 6.03955682e-05, 6.07291379e-05, 6.03663874e-05,\n",
-       "       6.09455354e-05, 6.04394784e-05, 5.99121208e-05, 6.06142926e-05,\n",
-       "       6.10353189e-05, 6.08828132e-05, 6.05075824e-05, 5.97776898e-05,\n",
-       "       6.08163489e-05, 6.02986852e-05, 6.09758254e-05, 6.07359576e-05,\n",
-       "       6.00931169e-05, 6.02027737e-05, 6.03320337e-05, 6.07413189e-05,\n",
-       "       6.08315504e-05, 6.07989603e-05, 6.00325076e-05, 6.06329579e-05,\n",
-       "       6.07120930e-05, 6.04848855e-05, 6.05827787e-05, 5.99284838e-05,\n",
-       "       6.08581603e-05, 6.05514144e-05, 6.09289059e-05, 6.07690518e-05,\n",
-       "       6.04421063e-05, 6.07723933e-05, 6.01957790e-05, 6.07062010e-05,\n",
-       "       6.06612006e-05, 6.05674758e-05, 6.02214697e-05, 6.03471110e-05,\n",
-       "       6.07673331e-05, 6.06226536e-05, 6.01751703e-05, 5.99976705e-05,\n",
-       "       6.10819481e-05, 6.04126752e-05, 6.06002418e-05, 6.08349537e-05,\n",
-       "       6.04606043e-05, 6.04499299e-05, 5.99651496e-05, 6.04658315e-05,\n",
-       "       6.06825595e-05, 6.07181048e-05, 6.03067106e-05, 6.05186665e-05,\n",
-       "       6.06103990e-05, 6.05233746e-05, 6.01243068e-05, 6.05934832e-05,\n",
-       "       6.06707120e-05, 6.02772529e-05, 6.04477546e-05, 6.03354426e-05,\n",
-       "       6.01718193e-05, 6.03790328e-05, 6.00252552e-05, 6.06133132e-05,\n",
-       "       6.06890936e-05, 6.06533027e-05, 6.04539699e-05, 6.03866911e-05,\n",
-       "       6.02894302e-05, 6.03911839e-05, 6.04399735e-05, 6.06630958e-05,\n",
-       "       6.04505153e-05, 6.04402936e-05, 6.07217674e-05, 6.01913089e-05,\n",
-       "       6.02269824e-05, 6.05460314e-05, 6.03687396e-05, 6.05909586e-05,\n",
-       "       6.06292351e-05, 6.05459065e-05, 6.04256577e-05, 6.02691297e-05,\n",
-       "       6.04486321e-05, 6.09440031e-05, 6.05334896e-05, 6.04438262e-05,\n",
-       "       6.02191197e-05, 6.04136527e-05, 6.10778232e-05, 6.02752226e-05,\n",
-       "       6.01695956e-05, 6.03315818e-05, 6.02956632e-05, 6.04598486e-05,\n",
-       "       6.07005269e-05, 6.07737217e-05, 6.04527036e-05, 6.06617866e-05,\n",
-       "       6.04111929e-05, 6.06943358e-05, 6.01907292e-05, 5.99306688e-05,\n",
-       "       6.04548129e-05, 6.05352018e-05, 6.06950458e-05, 6.06301440e-05,\n",
-       "       6.02304897e-05, 6.04988998e-05, 6.04180423e-05, 6.05479159e-05,\n",
-       "       6.08285294e-05, 6.06061759e-05, 6.04869635e-05, 6.05180254e-05,\n",
-       "       6.02648193e-05, 6.04929856e-05, 6.06716903e-05, 6.02299032e-05,\n",
-       "       6.04137189e-05, 6.08144515e-05, 6.07116836e-05, 6.07314909e-05,\n",
-       "       6.04547132e-05, 6.06094749e-05, 6.00797934e-05, 6.05719724e-05,\n",
-       "       6.06454037e-05, 6.07048546e-05, 6.02661062e-05, 6.04030963e-05,\n",
-       "       6.06327095e-05, 6.03159996e-05, 6.06098639e-05, 6.01867586e-05,\n",
-       "       6.02830444e-05, 6.06142720e-05, 6.05494269e-05, 6.08075423e-05,\n",
-       "       6.02474467e-05, 6.07159687e-05, 6.04234549e-05, 6.10337233e-05,\n",
-       "       6.05499466e-05, 6.08315832e-05, 6.05803575e-05, 6.05270859e-05,\n",
-       "       6.09122451e-05, 6.05156407e-05, 6.05511002e-05, 6.02266561e-05,\n",
-       "       6.03139092e-05, 6.05383356e-05, 6.04068495e-05, 6.07586002e-05,\n",
-       "       6.05368636e-05, 6.01698126e-05, 6.06129039e-05, 6.10347383e-05,\n",
-       "       6.07359125e-05, 6.08155221e-05, 6.05850918e-05, 6.07323895e-05,\n",
-       "       6.05697071e-05, 6.05346793e-05, 6.03198701e-05, 6.02876032e-05,\n",
-       "       6.02613477e-05, 6.05660457e-05, 6.03544703e-05, 6.07900712e-05,\n",
-       "       6.07106515e-05, 6.03237459e-05, 6.08457012e-05, 6.07828146e-05,\n",
-       "       6.07328905e-05, 6.07089784e-05, 6.11114828e-05, 6.05708697e-05,\n",
-       "       6.05578115e-05, 6.04171691e-05, 6.00725528e-05, 6.05568368e-05,\n",
-       "       6.01070988e-05, 6.01586468e-05, 6.02973878e-05, 6.05997526e-05,\n",
-       "       6.04794439e-05, 6.05291428e-05, 6.11416841e-05, 6.08278623e-05,\n",
-       "       6.08223460e-05, 6.02945268e-05, 6.05031389e-05, 6.05478298e-05,\n",
-       "       6.05456644e-05, 6.07837530e-05, 6.03080161e-05, 6.05796548e-05,\n",
-       "       6.02355940e-05, 6.01988407e-05, 6.05210709e-05, 6.05888182e-05,\n",
-       "       6.06243506e-05, 6.03127006e-05, 6.10239099e-05, 6.06355897e-05,\n",
-       "       6.07031726e-05, 6.04810249e-05, 6.07180309e-05, 6.00106191e-05,\n",
-       "       6.05767062e-05, 6.04583984e-05, 6.05041984e-05, 6.05797987e-05,\n",
-       "       6.04532780e-05, 6.03644564e-05, 6.04385164e-05, 6.07104778e-05,\n",
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-       "       6.08979853e-05, 6.03597879e-05, 6.04958030e-05, 6.04033567e-05,\n",
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-       "       6.05775999e-05, 6.05461090e-05, 6.05619352e-05, 6.07000120e-05,\n",
-       "       6.02642846e-05, 6.05418256e-05, 6.05731671e-05, 6.09024471e-05,\n",
-       "       6.07843065e-05, 6.04296071e-05, 6.08929951e-05, 6.04501828e-05,\n",
-       "       6.00776649e-05, 6.05842215e-05, 6.05907817e-05, 6.03715879e-05,\n",
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-       "       6.01890596e-05, 6.03493364e-05, 6.05873359e-05, 6.02537944e-05,\n",
-       "       6.09091374e-05, 6.06428577e-05, 6.07357281e-05, 6.04536112e-05,\n",
-       "       6.06347228e-05, 6.02317971e-05, 6.08653208e-05, 6.00390755e-05,\n",
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-       "       6.09168018e-05, 6.04749883e-05, 6.04347582e-05, 6.04889094e-05,\n",
-       "       6.06314168e-05, 6.07958106e-05, 6.06179062e-05, 6.08520282e-05,\n",
-       "       6.06803209e-05, 6.00488786e-05, 6.05011855e-05, 6.07600898e-05,\n",
-       "       6.05369161e-05, 6.09094951e-05, 6.09123336e-05, 6.04338320e-05,\n",
-       "       6.06823813e-05, 6.06006326e-05, 6.07524730e-05, 6.08205773e-05,\n",
-       "       6.05037387e-05, 6.06096676e-05, 6.03005311e-05, 6.10534592e-05,\n",
-       "       6.08219339e-05, 5.99417367e-05, 6.01391190e-05, 6.03184811e-05,\n",
-       "       6.03763547e-05, 6.05195621e-05, 6.06044090e-05, 6.07294402e-05,\n",
-       "       6.03771950e-05, 6.09271790e-05, 6.01831290e-05, 6.08792081e-05,\n",
-       "       6.05163854e-05, 6.06433959e-05, 5.98609808e-05, 6.08850714e-05,\n",
-       "       6.08738308e-05, 6.03917875e-05, 6.03941368e-05, 6.02790389e-05,\n",
-       "       6.03568645e-05, 6.08923305e-05, 6.05279284e-05, 6.06340410e-05,\n",
-       "       6.06051844e-05, 6.06528085e-05, 6.03416973e-05, 6.07194295e-05,\n",
-       "       6.03422742e-05, 6.07897454e-05, 6.02831149e-05, 6.06525885e-05,\n",
-       "       6.08680163e-05, 6.03893363e-05, 6.09657544e-05, 6.02066394e-05,\n",
-       "       6.01447042e-05, 6.06083225e-05, 6.06279059e-05, 6.06646893e-05,\n",
-       "       6.06150938e-05, 6.03631498e-05, 6.04788024e-05, 6.07411468e-05,\n",
-       "       6.03337604e-05, 6.06084889e-05, 6.06296874e-05, 6.06973287e-05,\n",
-       "       6.06802096e-05, 6.04538437e-05, 6.07501342e-05, 6.05338622e-05,\n",
-       "       6.03630961e-05, 6.06940610e-05, 6.05430936e-05, 6.02940403e-05,\n",
-       "       6.06876157e-05, 6.06484642e-05, 6.05377648e-05, 6.07070485e-05])</pre></li></ul></li></ul></div></div>"
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-       "<scipp.Variable> (samples: 800)    float64         [cm^2/s]  [6.04126e-05, 6.07504e-05, ..., 6.05378e-05, 6.0707e-05]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "yh.D_infinite"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "show-viscosity",
    "metadata": {},
-   "outputs": [
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dt:hover{overflow:visible;width:auto;z-index:1;}.sc-var-attrs{display:block;}.sc-var-data,.reveal .sc-var-data{display:none;}.sc-var-attrs,.sc-var-data{background-color:var(--sc-background-color0) !important;padding-bottom:5px !important;}.sc-var-attrs-in:checked ~ .sc-var-attrs{display:none;}.sc-var-data-in:checked ~ .sc-var-data{display:block;}.sc-var-data > table{float:right;}.sc-var-name span,.sc-var-data{padding-left:25px !important;}.sc-var-attrs,.sc-var-data{grid-column:1 / -1;}dl.sc-attrs{padding:0;margin:0;display:grid;grid-template-columns:125px auto;}.sc-attrs dt,dd{padding:0;margin:0;float:left;padding-right:10px;width:auto;}.sc-attrs dt{font-weight:normal;grid-column:1;}.sc-attrs dt:hover span{display:inline-block;padding-right:10px;}.sc-attrs dd{grid-column:2;white-space:pre-wrap;word-break:break-all;}.sc-icon-database,.sc-icon-file-text2{display:inline-block;vertical-align:middle;width:1em;height:1.5em !important;stroke-width:0;stroke:currentColor;fill:currentColor;}label.sc-hide-icon svg{opacity:0;}.sc-standalone-var-name{grid-column:1/3;}.sc-standalone-var-name span{padding-left:25px;padding-right:10px;}.sc-title{font-weight:bold;font-size:1.5em;}.sc-subtitle{font-weight:normal;font-style:italic;text-align:left;font-size:1.2em;padding:1px;}.sc-label{fill:var(--sc-font-color0,#444444);text-anchor:middle;}.sc-name{fill:var(--sc-font-color0,#111111);}.sc-inset-line{stroke:var(--sc-font-color1);stroke-width:0.05;stroke-dasharray:0.2,0.2;}.sc-log-wrap{height:25ex;resize:vertical;overflow-y:scroll;display:flex;flex-direction:column-reverse;border:1px solid;border-color:var(--jp-border-color2);background-color:var(--sc-background-color1);}div.sc-log{line-height:2.5ex;}table.sc-log{table-layout:auto;border-collapse:collapse;}tr.sc-log:nth-child(even){background-color:var(--sc-background-color0);}tr.sc-log > td{vertical-align:top;padding-bottom:0.5ex;}.sc-log-time-stamp{min-width:22ch;font-family:var(--jp-code-font-family);color:var(--sc-font-color2);}.sc-log-level{min-width:10ch;}tr.sc-log-debug td.sc-log-level{color:var(--jp-accent-color1);}tr.sc-log-info td.sc-log-level{color:var(--jp-info-color1);}tr.sc-log-warning td.sc-log-level{color:var(--jp-warn-color1);}tr.sc-log-error td.sc-log-level{font-weight:bold;color:var(--jp-error-color2);}tr.sc-log-critical td.sc-log-level{font-weight:bold;color:var(--sc-background-color0);background-color:var(--jp-error-color1);}.sc-log-message{white-space:pre-wrap;width:100%;}.sc-log-html-payload{white-space:normal;}.sc-log-name{padding-right:0.5em;text-align:right;white-space:pre-wrap;color:var(--sc-font-color3);}</style><div class=\"sc-wrap sc-root\"><div class=\"sc-header\"><div class=\"sc-obj-type\">kinisi.Samples</div></div><ul class=\"sc-sections\"><li class=\"sc-section-item\"><ul class=\"sc-var-list\"><li class=\"sc-var-item\"><div class=\"sc-standalone-var-name\"><span>(samples: 800)</span></div><div class=\"sc-var-dtype\">float64</div><div class=\"sc-var-unit\">Pa*s</div><div class=\"sc-value-preview sc-preview\">0.000285+/-0.000011</div><input class=\"sc-var-attrs-in\" disabled=\"\" id=\"attrs-c7fa1379-eb00-438f-9802-da80cd8c2cfe\" type=\"checkbox\"/><label class=\"sc-hide-icon\" for=\"attrs-c7fa1379-eb00-438f-9802-da80cd8c2cfe\" title=\"Show/Hide attributes\"><svg class=\"icon sc-icon-file-text2\"><use xlink:href=\"#icon-file-text2\"></use></svg></label><input class=\"sc-var-data-in\" id=\"data-4bea47c5-6ee8-456e-91b3-97bc2c5a94f4\" type=\"checkbox\"/><label for=\"data-4bea47c5-6ee8-456e-91b3-97bc2c5a94f4\" title=\"Show/Hide data repr\"><svg class=\"icon sc-icon-database\"><use xlink:href=\"#icon-database\"></use></svg></label><pre class=\"sc-var-data\">Values:<br/>array([0.00029584, 0.00027411, 0.00031553, 0.00027227, 0.00030938,\n",
-       "       0.00029013, 0.00032496, 0.00028264, 0.00026609, 0.00027102,\n",
-       "       0.00028593, 0.00029508, 0.00028137, 0.00029094, 0.0002765 ,\n",
-       "       0.00029895, 0.00029559, 0.00028883, 0.0003026 , 0.00027195,\n",
-       "       0.00029639, 0.00028597, 0.00029536, 0.0002636 , 0.00027505,\n",
-       "       0.00027311, 0.00028639, 0.00028778, 0.00029198, 0.00029425,\n",
-       "       0.00029657, 0.00028274, 0.00027019, 0.00028616, 0.00028736,\n",
-       "       0.00028097, 0.00029283, 0.00026827, 0.00030529, 0.00028181,\n",
-       "       0.00029922, 0.00027147, 0.00028323, 0.00029036, 0.00027965,\n",
-       "       0.00030011, 0.0003049 , 0.00028361, 0.00028201, 0.00028982,\n",
-       "       0.00028533, 0.00027023, 0.00026606, 0.00028657, 0.00029045,\n",
-       "       0.00028678, 0.00028967, 0.00027972, 0.00028225, 0.00028878,\n",
-       "       0.00028899, 0.00028469, 0.00029869, 0.00028801, 0.00027632,\n",
-       "       0.00029247, 0.00028925, 0.00027925, 0.00026743, 0.0002776 ,\n",
-       "       0.00030321, 0.00029179, 0.00028965, 0.00026231, 0.00027893,\n",
-       "       0.00029082, 0.00030254, 0.00028693, 0.00029774, 0.00028615,\n",
-       "       0.00028112, 0.00029629, 0.00029273, 0.00026192, 0.00028666,\n",
-       "       0.00028543, 0.00029615, 0.00028032, 0.00028638, 0.00026084,\n",
-       "       0.00028177, 0.00029438, 0.00030368, 0.00027661, 0.00029779,\n",
-       "       0.00029623, 0.00030153, 0.00029244, 0.00027716, 0.00027063,\n",
-       "       0.00028534, 0.00030009, 0.00029393, 0.00029313, 0.00027308,\n",
-       "       0.00028514, 0.00029107, 0.00029295, 0.00027323, 0.00027842,\n",
-       "       0.00029792, 0.00030357, 0.00029311, 0.00029596, 0.0002696 ,\n",
-       "       0.00029239, 0.00028682, 0.0002869 , 0.00028549, 0.00029481,\n",
-       "       0.00026636, 0.00028387, 0.00029308, 0.0002905 , 0.00026412,\n",
-       "       0.00028038, 0.00029259, 0.00029287, 0.00027993, 0.00028849,\n",
-       "       0.00026669, 0.0002865 , 0.00029232, 0.00028217, 0.00027791,\n",
-       "       0.00029607, 0.00028275, 0.00028898, 0.00030407, 0.00028511,\n",
-       "       0.00027335, 0.00028102, 0.00030058, 0.00029801, 0.00026709,\n",
-       "       0.00029864, 0.00027421, 0.00028694, 0.00030676, 0.00028956,\n",
-       "       0.00027294, 0.00029132, 0.00027226, 0.00028787, 0.00031473,\n",
-       "       0.000279  , 0.00026351, 0.00026613, 0.00028949, 0.00031717,\n",
-       "       0.00027493, 0.00029383, 0.00026725, 0.00027807, 0.00030342,\n",
-       "       0.00029675, 0.00029269, 0.00027193, 0.00027699, 0.00027612,\n",
-       "       0.00031433, 0.00028005, 0.0002773 , 0.00028364, 0.00028536,\n",
-       "       0.00031128, 0.00027237, 0.00028298, 0.00027107, 0.00028086,\n",
-       "       0.00029222, 0.00027224, 0.00030198, 0.0002795 , 0.00028296,\n",
-       "       0.00028539, 0.00030246, 0.00029336, 0.00027635, 0.00027403,\n",
-       "       0.00029847, 0.00030859, 0.00026701, 0.00028799, 0.00028378,\n",
-       "       0.00027674, 0.00028711, 0.00028868, 0.00031014, 0.0002899 ,\n",
-       "       0.00028063, 0.00027913, 0.0002943 , 0.00029064, 0.00028083,\n",
-       "       0.00028306, 0.00029897, 0.00028317, 0.00028137, 0.00029517,\n",
-       "       0.00028312, 0.00029675, 0.0003009 , 0.00029463, 0.00030613,\n",
-       "       0.0002797 , 0.00027588, 0.0002828 , 0.00028483, 0.00029518,\n",
-       "       0.00028386, 0.00029436, 0.0002883 , 0.00027945, 0.00028881,\n",
-       "       0.00029024, 0.0002748 , 0.00030463, 0.00029924, 0.00028608,\n",
-       "       0.00028572, 0.00027895, 0.00028168, 0.00028619, 0.00029151,\n",
-       "       0.00030543, 0.00028498, 0.00026751, 0.00028123, 0.00028919,\n",
-       "       0.00029966, 0.00029145, 0.00026426, 0.00029773, 0.00029927,\n",
-       "       0.00029401, 0.00029468, 0.00028765, 0.00027911, 0.00027771,\n",
-       "       0.00028767, 0.00028237, 0.00028612, 0.00027723, 0.00029483,\n",
-       "       0.00031478, 0.00028873, 0.00028237, 0.00028151, 0.00028265,\n",
-       "       0.00029462, 0.00028579, 0.00029377, 0.00028324, 0.00027268,\n",
-       "       0.00028198, 0.0002867 , 0.00028487, 0.00029677, 0.00028318,\n",
-       "       0.00028208, 0.00030322, 0.00028827, 0.0002777 , 0.00027948,\n",
-       "       0.00027831, 0.00028691, 0.0002806 , 0.00030417, 0.00028037,\n",
-       "       0.00027758, 0.000277  , 0.00029231, 0.00028753, 0.00027944,\n",
-       "       0.00028908, 0.00028393, 0.00030604, 0.00029549, 0.00028318,\n",
-       "       0.00028719, 0.00027848, 0.00029702, 0.00027577, 0.00029033,\n",
-       "       0.00026383, 0.00027755, 0.00027076, 0.00028014, 0.0002874 ,\n",
-       "       0.00027278, 0.00028376, 0.00028581, 0.0002999 , 0.00029293,\n",
-       "       0.00028326, 0.00029404, 0.00027823, 0.00028309, 0.00030031,\n",
-       "       0.00028357, 0.00026831, 0.00027631, 0.00027132, 0.00028422,\n",
-       "       0.00028109, 0.0002846 , 0.00028472, 0.00029377, 0.00029397,\n",
-       "       0.00029367, 0.00028156, 0.0002983 , 0.00027397, 0.00027596,\n",
-       "       0.00029551, 0.00027066, 0.00027498, 0.00027634, 0.00027802,\n",
-       "       0.0002644 , 0.00028597, 0.0002812 , 0.00028615, 0.00030396,\n",
-       "       0.0002836 , 0.00029826, 0.00029618, 0.00029713, 0.0002811 ,\n",
-       "       0.00028792, 0.00028409, 0.00026159, 0.00027364, 0.00027176,\n",
-       "       0.00029431, 0.00028701, 0.0002919 , 0.00028304, 0.00027275,\n",
-       "       0.00029048, 0.00028293, 0.00029674, 0.00029512, 0.0002882 ,\n",
-       "       0.00028068, 0.00028086, 0.00029234, 0.00026476, 0.00028237,\n",
-       "       0.00027659, 0.00028601, 0.00027769, 0.00030551, 0.00027926,\n",
-       "       0.00028405, 0.00028496, 0.00028296, 0.00028479, 0.00029331,\n",
-       "       0.00029044, 0.00027553, 0.0002937 , 0.00028769, 0.00027868,\n",
-       "       0.00027889, 0.00026796, 0.00028425, 0.00027854, 0.00030217,\n",
-       "       0.00027508, 0.00028957, 0.00028794, 0.00030984, 0.00027558,\n",
-       "       0.00029678, 0.00028948, 0.00027773, 0.00029095, 0.00029283,\n",
-       "       0.00028033, 0.00029078, 0.00027681, 0.00029667, 0.00026995,\n",
-       "       0.00028759, 0.00028212, 0.00029165, 0.00029199, 0.0002919 ,\n",
-       "       0.00028983, 0.00028946, 0.0002886 , 0.000267  , 0.00028077,\n",
-       "       0.00028544, 0.00027522, 0.00027528, 0.00027313, 0.000297  ,\n",
-       "       0.00027599, 0.0002912 , 0.00028723, 0.00029005, 0.00028615,\n",
-       "       0.00031037, 0.00030721, 0.00028294, 0.0002756 , 0.00029031,\n",
-       "       0.00026652, 0.0002816 , 0.0002705 , 0.00025524, 0.00026326,\n",
-       "       0.00030599, 0.00027895, 0.00027419, 0.00028704, 0.00029485,\n",
-       "       0.00029152, 0.00029263, 0.00028229, 0.00028069, 0.00027987,\n",
-       "       0.00031613, 0.00026586, 0.00028734, 0.00027362, 0.00026544,\n",
-       "       0.00026734, 0.00027362, 0.00027758, 0.00027666, 0.00028191,\n",
-       "       0.00027243, 0.00028733, 0.00029288, 0.00027411, 0.00028643,\n",
-       "       0.00027309, 0.00030734, 0.00029557, 0.00027793, 0.00027644,\n",
-       "       0.00028162, 0.00026574, 0.0002766 , 0.00029046, 0.00027606,\n",
-       "       0.00028217, 0.00029442, 0.00029841, 0.00029126, 0.00026515,\n",
-       "       0.00027615, 0.00027835, 0.00029223, 0.00029657, 0.00028572,\n",
-       "       0.00027686, 0.00027304, 0.00028104, 0.00029008, 0.00028757,\n",
-       "       0.00027477, 0.00028807, 0.00028602, 0.00029892, 0.00028114,\n",
-       "       0.00027683, 0.00028861, 0.00027539, 0.0003037 , 0.00029488,\n",
-       "       0.00027849, 0.00029243, 0.00028348, 0.00031067, 0.00029602,\n",
-       "       0.00026579, 0.00028618, 0.00028727, 0.00028613, 0.00029494,\n",
-       "       0.00027093, 0.00029036, 0.00028327, 0.00025871, 0.00029387,\n",
-       "       0.00029958, 0.00028088, 0.00028974, 0.00028729, 0.00030472,\n",
-       "       0.00029954, 0.00028058, 0.00028656, 0.00027549, 0.00027737,\n",
-       "       0.00028808, 0.00029753, 0.00028533, 0.00028455, 0.00028056,\n",
-       "       0.00029511, 0.00030454, 0.00028457, 0.00028838, 0.00029903,\n",
-       "       0.00028934, 0.0002785 , 0.00027799, 0.00029415, 0.00027863,\n",
-       "       0.00028222, 0.00027941, 0.00030697, 0.00028759, 0.00029277,\n",
-       "       0.0002846 , 0.00029516, 0.00031742, 0.00026618, 0.00027312,\n",
-       "       0.00029455, 0.00029962, 0.00028109, 0.00027602, 0.00029035,\n",
-       "       0.00028033, 0.00028517, 0.00027615, 0.00027644, 0.00028382,\n",
-       "       0.000268  , 0.00029049, 0.00029912, 0.0002983 , 0.00028779,\n",
-       "       0.00027836, 0.00030525, 0.0002996 , 0.00028763, 0.00027592,\n",
-       "       0.00029886, 0.00028369, 0.00028928, 0.0002879 , 0.00027467,\n",
-       "       0.000285  , 0.00027306, 0.0003009 , 0.00029814, 0.00027077,\n",
-       "       0.00026419, 0.00028504, 0.00029687, 0.00028536, 0.00029066,\n",
-       "       0.00028115, 0.00028581, 0.00028769, 0.00028298, 0.00027838,\n",
-       "       0.00028381, 0.00028233, 0.00029115, 0.00030675, 0.00030294,\n",
-       "       0.00028244, 0.00027254, 0.00027775, 0.00029635, 0.00027936,\n",
-       "       0.00029747, 0.00026405, 0.00028742, 0.00028834, 0.00026499,\n",
-       "       0.00028011, 0.00029317, 0.00027396, 0.00028722, 0.00028248,\n",
-       "       0.00029037, 0.00030996, 0.00028847, 0.00028356, 0.00028396,\n",
-       "       0.00027838, 0.00029326, 0.00026724, 0.00027752, 0.0002875 ,\n",
-       "       0.00026738, 0.00027422, 0.00027726, 0.00028643, 0.0002964 ,\n",
-       "       0.00029193, 0.00029323, 0.00030436, 0.0002799 , 0.00027604,\n",
-       "       0.00028646, 0.00028159, 0.00030965, 0.00026939, 0.00029811,\n",
-       "       0.00028832, 0.00028237, 0.00028508, 0.00027603, 0.00029392,\n",
-       "       0.00029493, 0.00028955, 0.00029226, 0.00029831, 0.00028968,\n",
-       "       0.00028495, 0.00030043, 0.00028213, 0.00032963, 0.00027351,\n",
-       "       0.00028297, 0.00030419, 0.0002827 , 0.00030193, 0.00027209,\n",
-       "       0.00026937, 0.00028758, 0.00028737, 0.00028801, 0.00029668,\n",
-       "       0.00027953, 0.0002799 , 0.00027931, 0.00028651, 0.00028275,\n",
-       "       0.00027865, 0.00027228, 0.00029828, 0.00028023, 0.00028156,\n",
-       "       0.00026815, 0.00027532, 0.00028392, 0.00027058, 0.00028979,\n",
-       "       0.00030569, 0.00027943, 0.00027973, 0.00029041, 0.00027247,\n",
-       "       0.00028061, 0.00026891, 0.00028476, 0.0003071 , 0.00028464,\n",
-       "       0.00028603, 0.00028039, 0.00027322, 0.0002834 , 0.00027494,\n",
-       "       0.00029543, 0.00028911, 0.00030001, 0.00026298, 0.00029343,\n",
-       "       0.00027998, 0.00027771, 0.00027348, 0.0002798 , 0.00030499,\n",
-       "       0.00028868, 0.00028075, 0.00029874, 0.00026926, 0.00027855,\n",
-       "       0.00027799, 0.0002893 , 0.00027904, 0.00030187, 0.0002708 ,\n",
-       "       0.00031162, 0.00030176, 0.00027721, 0.00026745, 0.00027549,\n",
-       "       0.0002768 , 0.00029899, 0.00028026, 0.00028067, 0.00029268,\n",
-       "       0.0002724 , 0.0002713 , 0.00030244, 0.00027418, 0.00031628,\n",
-       "       0.0002919 , 0.00029648, 0.00027764, 0.00027038, 0.00026267,\n",
-       "       0.0002956 , 0.00027203, 0.00028486, 0.00029332, 0.0002876 ,\n",
-       "       0.00027881, 0.00027171, 0.00028311, 0.0002704 , 0.00027062,\n",
-       "       0.00030842, 0.00028116, 0.00027947, 0.00028236, 0.00026767,\n",
-       "       0.00026758, 0.00028696, 0.00028151, 0.00028368, 0.00027488,\n",
-       "       0.00027643, 0.0002841 , 0.00027755, 0.00029692, 0.00025994,\n",
-       "       0.00026911, 0.00030675, 0.00029963, 0.00029815, 0.0002908 ,\n",
-       "       0.00028928, 0.00028293, 0.00027377, 0.00029199, 0.00026989,\n",
-       "       0.00029674, 0.00027309, 0.00028409, 0.00027727, 0.00031788,\n",
-       "       0.00026961, 0.00026532, 0.0002907 , 0.00028906, 0.00030185,\n",
-       "       0.00029084, 0.00027019, 0.00028798, 0.00028002, 0.00027983,\n",
-       "       0.00027941, 0.0002934 , 0.00027771, 0.00029003, 0.00027159,\n",
-       "       0.00029594, 0.00027762, 0.0002659 , 0.00028685, 0.00026727,\n",
-       "       0.00030464, 0.00030176, 0.00028052, 0.0002852 , 0.00027764,\n",
-       "       0.00027954, 0.0002943 , 0.00028644, 0.00027407, 0.00028886,\n",
-       "       0.00028281, 0.0002804 , 0.00027905, 0.00027583, 0.00028201,\n",
-       "       0.00027553, 0.00028601, 0.00029255, 0.00028078, 0.00028945,\n",
-       "       0.00029674, 0.00027595, 0.00027813, 0.00028494, 0.00028239])</pre></li></ul></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<scipp.Variable> (samples: 800)    float64           [Pa*s]  [0.00029584, 0.000274112, ..., 0.000284939, 0.000282393]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "yh.shear_viscosity"
    ]
@@ -675,21 +190,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "plot-results",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots()\n",
     "\n",
@@ -711,13 +215,12 @@
     ")\n",
     "\n",
     "D_0_samples = yh.data_group['D_0'].values\n",
-    "eta_samples = yh.data_group['viscosity'].values\n",
+    "slope_samples = yh.data_group['slope'].values\n",
     "n_samples = len(D_0_samples)\n",
     "predictions_extended = np.zeros((len(inv_L_extended), n_samples))\n",
     "\n",
-    "for i in range(len(yh.data_group['D_0'])):\n",
-    "    slope = yh.viscosity_to_slope(eta_samples[i] * yh.parameter_units[1])\n",
-    "    predictions_extended[:, i] = yh.yeh_hummer_linear(inv_L_extended, D_0_samples[i], slope)\n",
+    "for i in range(n_samples):\n",
+    "    predictions_extended[:, i] = D_0_samples[i] - slope_samples[i] * inv_L_extended\n",
     "\n",
     "for i, ci in enumerate(credible_intervals):\n",
     "    ax.fill_between(\n",
diff --git a/kinisi/fitting.py b/kinisi/fitting.py
index dd6b3bb..5ff1573 100644
--- a/kinisi/fitting.py
+++ b/kinisi/fitting.py
@@ -112,10 +112,6 @@ def log_likelihood(self, parameters: tuple[float]) -> float:
         x_values = self.get_independent_variable()
         model = self.function(x_values, *parameters)
 
-        # Handle invalid model values (e.g., from VTF when T0 >= T)
-        if np.any(~np.isfinite(model)) or np.any(model <= 0):
-            return -np.inf
-
         covariance_matrix = np.diag(self.data.variances)
         y_values = self.data.values
 
diff --git a/kinisi/yeh_hummer.py b/kinisi/yeh_hummer.py
index f3cfe49..ba2136e 100644
--- a/kinisi/yeh_hummer.py
+++ b/kinisi/yeh_hummer.py
@@ -12,7 +12,7 @@
 import numpy as np
 import scipp as sc
 import scipp.constants as const
-from scipy.optimize import curve_fit
+from scipy.optimize import curve_fit, minimize
 
 from kinisi import __version__
 from kinisi.fitting import FittingBase
@@ -99,7 +99,7 @@ def _yeh_hummer_function(
 
         :param box_lengths: Array of box lengths / Angstrom
         :param D_0: Infinite-system diffusion coefficient
-        :param slope: Slope = (k_B * T * xi) / (6 * pi * eta)
+        :param slope: i.e., (k_B * T * xi) / (6 * pi * eta)
         """
         return D_0 - slope / np.asarray(box_lengths)
 
@@ -159,8 +159,6 @@ def linear_func(x, a, b):
         # Use these as initial parameters for optimization
         x0 = [D_0_init, slope_init]
 
-        from scipy.optimize import minimize
-
         # Convert bounds to format expected by scipy
         bounds_scipy = [(b[0].value, b[1].value) for b in self.bounds]
         result = minimize(self.nll, x0, bounds=bounds_scipy, method='L-BFGS-B')
@@ -170,7 +168,7 @@ def linear_func(x, a, b):
         self.data_group['slope'] = result.x[1] * self.parameter_units[1]
 
     @property
-    def D_infinite(self):
+    def D_infinite(self) -> sc.Variable | Samples:
         """Return infinite-system diffusion coefficient."""
         return self.data_group['D_0']
 
@@ -179,11 +177,11 @@ def shear_viscosity(self) -> sc.Variable | Samples:
         """Return estimated shear viscosity (converted from slope samples)."""
         slope_data = self.data_group['slope']
         if isinstance(slope_data, Samples):
-            # Convert each slope sample to viscosity
-            viscosities = []
-            for s in slope_data.values:
-                eta = self._slope_to_viscosity(s)
-                viscosities.append(eta.value)
-            return Samples(np.array(viscosities), unit=sc.Unit('Pa*s'))
+            # eta = (k_B * T * xi) / (6 * pi * slope)
+            k_B_T = sc.to_unit(const.Boltzmann * self.temperature, 'J').value
+            slope_to_SI = sc.to_unit(1.0 * self._slope_unit, 'm^3/s').value
+            slopes_SI = slope_data.values * slope_to_SI
+            viscosities = (k_B_T * self.xi_cubic) / (6 * np.pi * slopes_SI)
+            return Samples(viscosities, unit=sc.Unit('Pa*s'))
         else:
             return self._slope_to_viscosity(slope_data.value)